Abstract
Accurate detection of cadmium (Cd) and lead (Pb)-induced cross-stress on crops is essential for agricultural, ecological environment, and food security. The feasibility to diagnose and predict Cd–Pb cross-stress in agricultural soil was explored by measuring the visible and near-infrared reflectance of rice leaves. In this study, two models were developed—namely a diagnostic model and a prediction model. The diagnostic model was established based on visible and near-infrared reflectance spectroscopy (VNIRS) datasets with Support Vector Machine (SVM), followed by leave-one-out cross-validation (LOOCV). A partial least-squares (PLS) regression, as the prediction model was employed to predict the foliar concentration of Cd and Pb contents. To accurately calibrate the two models, a rigorous greenhouse experiment was designed and implemented, with 4 levels of treatments on each of the Cd and Pb stress on rice. Results show that with the appropriate pre-processing, the diagnostic model can identify 79% of Cd and 85% of Pb stress of any levels. The significant bands that have been used mainly distributed between 681–776 nm and 1224–1349 nm for Cd stress and 712–784 nm for Pb stress. The prediction model can estimate Cd with coefficient of determination of 0.7, but failed to predict Pb accurately. The results illustrated the feasibility to diagnose Cd stress accurately by measuring the visible and near-infrared reflectance of rice canopy in a cross-contamination soil environment. This study serves as one step forward to heavy metal pollutant detection in a farmland environment.
Highlights
Over recent decades, the accumulation of heavy metals in agricultural soil has been an important issue worldwide related to environmental pollution and human risk [1]
The specific objectives of the current work were to: (1) acquire hyperspectral datasets of rice canopy stressed by Cd–Pb cross-stress with four different stress levels, ranging from 350 nm to 2500 nm; (2) establish discrimination models to diagnose the types and the stress levels and investigate the diagnostic ability between single pretreatments and combined pretreatments and (3) establish prediction models to predict the Cd–Pb contents in rice leaf
To conform to the performance of predicted results, we investigated some research about estimating the Cd contents and Pb contents by VINRS in recent years
Summary
The accumulation of heavy metals in agricultural soil has been an important issue worldwide related to environmental pollution and human risk [1]. VNIRS (350 nm to 2500 nm) is a highly efficient and non-destructive tool for ecological applications, which allows qualitative and quantitative analysis to be implemented in different matrices It can be used, to estimate soil properties by soils hyperspectral reflectance, such as clay [5], moisture [6], organic matter [4,7,8] and iron oxides [9], but to estimate heavy metals concentration by soils hyperspectral spectra, such as Pb [4,10,11], Cd [7,10,12], zinc (Zn) [7,13], copper (Cu) [9,13,14], mercury (Hg) [15,16] and so on, in agricultural soils
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